Construction of Power Communication Network Knowledge Graph with BERT-BiLSTM-CRF Model Based Entity Recognition

Abstract

By extensively mining system data and integrating with artificial intelligence means, knowledge graph can be exploited in various tasks of power communication network, effectively prompting the efficiency and performance of maintenance. One of the pivotal step of the knowledge graph construction is the named entity recognition. Abundant semantic features extracted from corpus can directly improve the accuracy of resulting concepts in knowledge graph. However, existing entity recognition method is mainly based on conventional word embedding technique such as Word2Vec, which still focuses on information within single word. In this paper, we propose to construct knowledge graph with the most recently proposed BERT-BiLSTM-CRF. This model can fully consider contextual information over words and extract more semantic features for further procedures. Our experimental results on realistic maintenance data of power communication networks proved the efficacy of BERT-BiLSTM-CRF model in the construction of knowledge graph. With the assistance of knowledge graph, we build applications for two typical maintenance scenarios, process standardization and fault disposal instruction, respectively. The knowledge graph has shown promising prospect as a novel auxiliary mechanism to power communication networks.